On Representation Redundancy in Large-Scale Instruction Tuning Data Selection

• URL: http://arxiv.org/abs/2602.13773v1
• Date: Sat, 14 Feb 2026 13:35:34 GMT
• Title: On Representation Redundancy in Large-Scale Instruction Tuning Data Selection
• Authors: Youwei Shu, Shaomian Zheng, Dingnan Jin, Wenjie Qu, Ziyao Guo, Qing Cui, Jun Zhou, Jiaheng Zhang,
• Abstract summary: We study instruction-tuning data selection through the lens of semantic representation similarity.<n>We propose Compressed Representation Data Selection (CRDS), a novel framework with two variants.<n> Experimental results demonstrate that both variants substantially enhance data quality and consistently outperform state-of-the-art representation-based selection methods.
• Score: 20.850719141827664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data quality is a crucial factor in large language models training. While prior work has shown that models trained on smaller, high-quality datasets can outperform those trained on much larger but noisy or low-quality corpora, systematic methods for industrial-scale data selection in instruction tuning remain underexplored. In this work, we study instruction-tuning data selection through the lens of semantic representation similarity and identify a key limitation of state-of-the-art LLM encoders: they produce highly redundant semantic embeddings. To mitigate this redundancy, we propose Compressed Representation Data Selection (CRDS), a novel framework with two variants. CRDS-R applies Rademacher random projection followed by concatenation of transformer hidden-layer representations, while CRDS-W employs whitening-based dimensionality reduction to improve representational quality. Experimental results demonstrate that both variants substantially enhance data quality and consistently outperform state-of-the-art representation-based selection methods. Notably, CRDS-W achieves strong performance using only 3.5% of the data, surpassing the full-data baseline by an average of 0.71% across four datasets. Our code is available at https://github.com/tdano1/CRDS.

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